{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.transforms import RandAugment\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import time\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from HighwayNet import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    " ## 修改模型\n",
    "net = HighwayNet(drop = 0.2).to(device)\n",
    "num_epochs=50\n",
    " # 定义损失函数和优化器\n",
    "trainloader,testloader = get_data_loader()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/49 轮训练\n",
      "----------\n",
      "train 损失: 4.2247 Top-1 准确率: 0.0658 Top-5 准确率: 0.2153\n",
      "val 损失: 4.2019 Top-1 准确率: 0.0595 Top-5 准确率: 0.2032\n",
      "\n",
      "第 1/49 轮训练\n",
      "----------\n",
      "train 损失: 3.8491 Top-1 准确率: 0.1242 Top-5 准确率: 0.3398\n",
      "val 损失: 3.9164 Top-1 准确率: 0.0952 Top-5 准确率: 0.3015\n",
      "\n",
      "第 2/49 轮训练\n",
      "----------\n",
      "train 损失: 3.5017 Top-1 准确率: 0.1892 Top-5 准确率: 0.4402\n",
      "val 损失: 3.7378 Top-1 准确率: 0.1210 Top-5 准确率: 0.3750\n",
      "\n",
      "第 3/49 轮训练\n",
      "----------\n",
      "train 损失: 3.2400 Top-1 准确率: 0.2431 Top-5 准确率: 0.5071\n",
      "val 损失: 3.2035 Top-1 准确率: 0.2002 Top-5 准确率: 0.4993\n",
      "\n",
      "第 4/49 轮训练\n",
      "----------\n",
      "train 损失: 3.0323 Top-1 准确率: 0.2852 Top-5 准确率: 0.5532\n",
      "val 损失: 2.9022 Top-1 准确率: 0.2741 Top-5 准确率: 0.5748\n",
      "\n",
      "第 5/49 轮训练\n",
      "----------\n",
      "train 损失: 2.8800 Top-1 准确率: 0.3207 Top-5 准确率: 0.5889\n",
      "val 损失: 2.6227 Top-1 准确率: 0.3161 Top-5 准确率: 0.6530\n",
      "\n",
      "第 6/49 轮训练\n",
      "----------\n",
      "train 损失: 2.7656 Top-1 准确率: 0.3463 Top-5 准确率: 0.6135\n",
      "val 损失: 2.4721 Top-1 准确率: 0.3500 Top-5 准确率: 0.6822\n",
      "\n",
      "第 7/49 轮训练\n",
      "----------\n",
      "train 损失: 2.6481 Top-1 准确率: 0.3705 Top-5 准确率: 0.6357\n",
      "val 损失: 2.4394 Top-1 准确率: 0.3650 Top-5 准确率: 0.6840\n",
      "\n",
      "第 8/49 轮训练\n",
      "----------\n",
      "train 损失: 2.5530 Top-1 准确率: 0.3940 Top-5 准确率: 0.6524\n",
      "val 损失: 2.1693 Top-1 准确率: 0.4292 Top-5 准确率: 0.7461\n",
      "\n",
      "第 9/49 轮训练\n",
      "----------\n",
      "train 损失: 2.4843 Top-1 准确率: 0.4083 Top-5 准确率: 0.6653\n",
      "val 损失: 2.1481 Top-1 准确率: 0.4285 Top-5 准确率: 0.7455\n",
      "\n",
      "第 10/49 轮训练\n",
      "----------\n",
      "train 损失: 2.4064 Top-1 准确率: 0.4259 Top-5 准确率: 0.6806\n",
      "val 损失: 2.2259 Top-1 准确率: 0.4125 Top-5 准确率: 0.7292\n",
      "\n",
      "第 11/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3436 Top-1 准确率: 0.4442 Top-5 准确率: 0.6934\n",
      "val 损失: 2.0695 Top-1 准确率: 0.4533 Top-5 准确率: 0.7588\n",
      "\n",
      "第 12/49 轮训练\n",
      "----------\n",
      "train 损失: 2.2876 Top-1 准确率: 0.4541 Top-5 准确率: 0.7046\n",
      "val 损失: 1.9552 Top-1 准确率: 0.4766 Top-5 准确率: 0.7809\n",
      "\n",
      "第 13/49 轮训练\n",
      "----------\n",
      "train 损失: 2.2214 Top-1 准确率: 0.4705 Top-5 准确率: 0.7150\n",
      "val 损失: 2.0460 Top-1 准确率: 0.4619 Top-5 准确率: 0.7548\n",
      "\n",
      "第 14/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1842 Top-1 准确率: 0.4767 Top-5 准确率: 0.7276\n",
      "val 损失: 1.9278 Top-1 准确率: 0.4853 Top-5 准确率: 0.7841\n",
      "\n",
      "第 15/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1425 Top-1 准确率: 0.4870 Top-5 准确率: 0.7357\n",
      "val 损失: 1.8045 Top-1 准确率: 0.5087 Top-5 准确率: 0.8123\n",
      "\n",
      "第 16/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0973 Top-1 准确率: 0.4958 Top-5 准确率: 0.7460\n",
      "val 损失: 1.7258 Top-1 准确率: 0.5350 Top-5 准确率: 0.8214\n",
      "\n",
      "第 17/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0372 Top-1 准确率: 0.5108 Top-5 准确率: 0.7554\n",
      "val 损失: 1.8467 Top-1 准确率: 0.4968 Top-5 准确率: 0.8039\n",
      "\n",
      "第 18/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0234 Top-1 准确率: 0.5131 Top-5 准确率: 0.7622\n",
      "val 损失: 1.6845 Top-1 准确率: 0.5413 Top-5 准确率: 0.8327\n",
      "\n",
      "第 19/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9887 Top-1 准确率: 0.5213 Top-5 准确率: 0.7701\n",
      "val 损失: 1.7722 Top-1 准确率: 0.5234 Top-5 准确率: 0.8115\n",
      "\n",
      "第 20/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9597 Top-1 准确率: 0.5272 Top-5 准确率: 0.7779\n",
      "val 损失: 1.6810 Top-1 准确率: 0.5414 Top-5 准确率: 0.8334\n",
      "\n",
      "第 21/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9262 Top-1 准确率: 0.5338 Top-5 准确率: 0.7844\n",
      "val 损失: 1.6723 Top-1 准确率: 0.5458 Top-5 准确率: 0.8276\n",
      "\n",
      "第 22/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8836 Top-1 准确率: 0.5448 Top-5 准确率: 0.7929\n",
      "val 损失: 1.6114 Top-1 准确率: 0.5632 Top-5 准确率: 0.8389\n",
      "\n",
      "第 23/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8599 Top-1 准确率: 0.5477 Top-5 准确率: 0.7978\n",
      "val 损失: 1.5601 Top-1 准确率: 0.5708 Top-5 准确率: 0.8476\n",
      "\n",
      "第 24/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8336 Top-1 准确率: 0.5551 Top-5 准确率: 0.8057\n",
      "val 损失: 1.4497 Top-1 准确率: 0.5994 Top-5 准确率: 0.8663\n",
      "\n",
      "第 25/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8086 Top-1 准确率: 0.5609 Top-5 准确率: 0.8101\n",
      "val 损失: 1.6676 Top-1 准确率: 0.5518 Top-5 准确率: 0.8295\n",
      "\n",
      "第 26/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7833 Top-1 准确率: 0.5693 Top-5 准确率: 0.8134\n",
      "val 损失: 1.5772 Top-1 准确率: 0.5696 Top-5 准确率: 0.8428\n",
      "\n",
      "第 27/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7485 Top-1 准确率: 0.5761 Top-5 准确率: 0.8232\n",
      "val 损失: 1.5002 Top-1 准确率: 0.5802 Top-5 准确率: 0.8629\n",
      "\n",
      "第 28/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7261 Top-1 准确率: 0.5812 Top-5 准确率: 0.8252\n",
      "val 损失: 1.4609 Top-1 准确率: 0.6063 Top-5 准确率: 0.8624\n",
      "\n",
      "第 29/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7157 Top-1 准确率: 0.5827 Top-5 准确率: 0.8315\n",
      "val 损失: 1.5481 Top-1 准确率: 0.5794 Top-5 准确率: 0.8535\n",
      "\n",
      "第 30/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6902 Top-1 准确率: 0.5880 Top-5 准确率: 0.8363\n",
      "val 损失: 1.3104 Top-1 准确率: 0.6291 Top-5 准确率: 0.8870\n",
      "\n",
      "第 31/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6621 Top-1 准确率: 0.5933 Top-5 准确率: 0.8442\n",
      "val 损失: 1.3852 Top-1 准确率: 0.6196 Top-5 准确率: 0.8767\n",
      "\n",
      "第 32/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6559 Top-1 准确率: 0.5955 Top-5 准确率: 0.8451\n",
      "val 损失: 1.2995 Top-1 准确率: 0.6406 Top-5 准确率: 0.8894\n",
      "\n",
      "第 33/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6287 Top-1 准确率: 0.6018 Top-5 准确率: 0.8500\n",
      "val 损失: 1.3869 Top-1 准确率: 0.6196 Top-5 准确率: 0.8773\n",
      "\n",
      "第 34/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6193 Top-1 准确率: 0.6053 Top-5 准确率: 0.8531\n",
      "val 损失: 1.4711 Top-1 准确率: 0.5993 Top-5 准确率: 0.8682\n",
      "\n",
      "第 35/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5900 Top-1 准确率: 0.6100 Top-5 准确率: 0.8587\n",
      "val 损失: 1.3252 Top-1 准确率: 0.6316 Top-5 准确率: 0.8847\n",
      "\n",
      "第 36/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5725 Top-1 准确率: 0.6161 Top-5 准确率: 0.8634\n",
      "val 损失: 1.3963 Top-1 准确率: 0.6269 Top-5 准确率: 0.8674\n",
      "\n",
      "第 37/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5330 Top-1 准确率: 0.6253 Top-5 准确率: 0.8698\n",
      "val 损失: 1.3049 Top-1 准确率: 0.6485 Top-5 准确率: 0.8848\n",
      "\n",
      "第 38/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5315 Top-1 准确率: 0.6235 Top-5 准确率: 0.8714\n",
      "val 损失: 1.4223 Top-1 准确率: 0.6159 Top-5 准确率: 0.8749\n",
      "\n",
      "第 39/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5028 Top-1 准确率: 0.6314 Top-5 准确率: 0.8759\n",
      "val 损失: 1.3758 Top-1 准确率: 0.6211 Top-5 准确率: 0.8748\n",
      "\n",
      "第 40/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4896 Top-1 准确率: 0.6331 Top-5 准确率: 0.8779\n",
      "val 损失: 1.3755 Top-1 准确率: 0.6251 Top-5 准确率: 0.8756\n",
      "\n",
      "第 41/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4861 Top-1 准确率: 0.6322 Top-5 准确率: 0.8839\n",
      "val 损失: 1.2009 Top-1 准确率: 0.6707 Top-5 准确率: 0.9021\n",
      "\n",
      "第 42/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4574 Top-1 准确率: 0.6424 Top-5 准确率: 0.8881\n",
      "val 损失: 1.2780 Top-1 准确率: 0.6545 Top-5 准确率: 0.8881\n",
      "\n",
      "第 43/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4371 Top-1 准确率: 0.6446 Top-5 准确率: 0.8898\n",
      "val 损失: 1.2821 Top-1 准确率: 0.6570 Top-5 准确率: 0.8849\n",
      "\n",
      "第 44/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4297 Top-1 准确率: 0.6472 Top-5 准确率: 0.8942\n",
      "val 损失: 1.2247 Top-1 准确率: 0.6626 Top-5 准确率: 0.8980\n",
      "\n",
      "第 45/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4215 Top-1 准确率: 0.6467 Top-5 准确率: 0.8954\n",
      "val 损失: 1.2139 Top-1 准确率: 0.6685 Top-5 准确率: 0.8972\n",
      "\n",
      "第 46/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4052 Top-1 准确率: 0.6523 Top-5 准确率: 0.8981\n",
      "val 损失: 1.1840 Top-1 准确率: 0.6802 Top-5 准确率: 0.9073\n",
      "\n",
      "第 47/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3836 Top-1 准确率: 0.6568 Top-5 准确率: 0.9043\n",
      "val 损失: 1.3567 Top-1 准确率: 0.6395 Top-5 准确率: 0.8762\n",
      "\n",
      "第 48/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3818 Top-1 准确率: 0.6584 Top-5 准确率: 0.9044\n",
      "val 损失: 1.2620 Top-1 准确率: 0.6673 Top-5 准确率: 0.8900\n",
      "\n",
      "第 49/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3621 Top-1 准确率: 0.6624 Top-5 准确率: 0.9070\n",
      "val 损失: 1.2791 Top-1 准确率: 0.6562 Top-5 准确率: 0.8898\n",
      "\n",
      "训练完成，耗时 81m 19s\n",
      "最佳验证集准确率: 0.680200\n"
     ]
    }
   ],
   "source": [
    "model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/9 轮训练\n",
      "----------\n",
      "train 损失: 1.2179 Top-1 准确率: 0.6985 Top-5 准确率: 0.9195\n",
      "val 损失: 0.9882 Top-1 准确率: 0.7259 Top-5 准确率: 0.9273\n",
      "\n",
      "第 1/9 轮训练\n",
      "----------\n",
      "train 损失: 1.1474 Top-1 准确率: 0.7145 Top-5 准确率: 0.9338\n",
      "val 损失: 0.9731 Top-1 准确率: 0.7324 Top-5 准确率: 0.9292\n",
      "\n",
      "第 2/9 轮训练\n",
      "----------\n",
      "train 损失: 1.1219 Top-1 准确率: 0.7219 Top-5 准确率: 0.9373\n",
      "val 损失: 0.9588 Top-1 准确率: 0.7374 Top-5 准确率: 0.9319\n",
      "\n",
      "第 3/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0913 Top-1 准确率: 0.7294 Top-5 准确率: 0.9426\n",
      "val 损失: 0.9489 Top-1 准确率: 0.7386 Top-5 准确率: 0.9301\n",
      "\n",
      "第 4/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0960 Top-1 准确率: 0.7281 Top-5 准确率: 0.9453\n",
      "val 损失: 0.9453 Top-1 准确率: 0.7401 Top-5 准确率: 0.9302\n",
      "\n",
      "第 5/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0806 Top-1 准确率: 0.7302 Top-5 准确率: 0.9481\n",
      "val 损失: 0.9441 Top-1 准确率: 0.7405 Top-5 准确率: 0.9323\n",
      "\n",
      "第 6/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0711 Top-1 准确率: 0.7326 Top-5 准确率: 0.9491\n",
      "val 损失: 0.9287 Top-1 准确率: 0.7479 Top-5 准确率: 0.9336\n",
      "\n",
      "第 7/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0605 Top-1 准确率: 0.7357 Top-5 准确率: 0.9526\n",
      "val 损失: 0.9386 Top-1 准确率: 0.7436 Top-5 准确率: 0.9342\n",
      "\n",
      "第 8/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0522 Top-1 准确率: 0.7367 Top-5 准确率: 0.9547\n",
      "val 损失: 0.9313 Top-1 准确率: 0.7441 Top-5 准确率: 0.9350\n",
      "\n",
      "第 9/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0369 Top-1 准确率: 0.7409 Top-5 准确率: 0.9567\n",
      "val 损失: 0.9321 Top-1 准确率: 0.7478 Top-5 准确率: 0.9350\n",
      "\n",
      "训练完成，耗时 16m 35s\n",
      "最佳验证集准确率: 0.747900\n"
     ]
    }
   ],
   "source": [
    "model2,(train_losses2, train_top1_accs2, train_top5_accs2, val_losses2, val_top1_accs2, val_top5_accs2) = train_model(model,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 10)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0543 Top-1 准确率: 0.7365 Top-5 准确率: 0.9510\n",
      "val 损失: 0.9468 Top-1 准确率: 0.7408 Top-5 准确率: 0.9330\n",
      "\n",
      "第 1/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0677 Top-1 准确率: 0.7325 Top-5 准确率: 0.9548\n",
      "val 损失: 0.9302 Top-1 准确率: 0.7459 Top-5 准确率: 0.9351\n",
      "\n",
      "第 2/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0445 Top-1 准确率: 0.7397 Top-5 准确率: 0.9559\n",
      "val 损失: 0.9347 Top-1 准确率: 0.7441 Top-5 准确率: 0.9337\n",
      "\n",
      "第 3/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0398 Top-1 准确率: 0.7395 Top-5 准确率: 0.9573\n",
      "val 损失: 0.9374 Top-1 准确率: 0.7432 Top-5 准确率: 0.9339\n",
      "\n",
      "第 4/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0237 Top-1 准确率: 0.7419 Top-5 准确率: 0.9594\n",
      "val 损失: 0.9299 Top-1 准确率: 0.7436 Top-5 准确率: 0.9345\n",
      "\n",
      "第 5/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0228 Top-1 准确率: 0.7433 Top-5 准确率: 0.9595\n",
      "val 损失: 0.9249 Top-1 准确率: 0.7449 Top-5 准确率: 0.9350\n",
      "\n",
      "第 6/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0075 Top-1 准确率: 0.7477 Top-5 准确率: 0.9615\n",
      "val 损失: 0.9360 Top-1 准确率: 0.7430 Top-5 准确率: 0.9333\n",
      "\n",
      "第 7/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0108 Top-1 准确率: 0.7458 Top-5 准确率: 0.9636\n",
      "val 损失: 0.9271 Top-1 准确率: 0.7466 Top-5 准确率: 0.9336\n",
      "\n",
      "第 8/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0102 Top-1 准确率: 0.7449 Top-5 准确率: 0.9647\n",
      "val 损失: 0.9309 Top-1 准确率: 0.7471 Top-5 准确率: 0.9339\n",
      "\n",
      "第 9/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0043 Top-1 准确率: 0.7468 Top-5 准确率: 0.9648\n",
      "val 损失: 0.9415 Top-1 准确率: 0.7434 Top-5 准确率: 0.9318\n",
      "\n",
      "第 10/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9849 Top-1 准确率: 0.7526 Top-5 准确率: 0.9674\n",
      "val 损失: 0.9232 Top-1 准确率: 0.7482 Top-5 准确率: 0.9361\n",
      "\n",
      "第 11/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9901 Top-1 准确率: 0.7503 Top-5 准确率: 0.9663\n",
      "val 损失: 0.9297 Top-1 准确率: 0.7450 Top-5 准确率: 0.9334\n",
      "\n",
      "第 12/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9785 Top-1 准确率: 0.7540 Top-5 准确率: 0.9691\n",
      "val 损失: 0.9329 Top-1 准确率: 0.7446 Top-5 准确率: 0.9339\n",
      "\n",
      "第 13/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9745 Top-1 准确率: 0.7531 Top-5 准确率: 0.9687\n",
      "val 损失: 0.9274 Top-1 准确率: 0.7473 Top-5 准确率: 0.9346\n",
      "\n",
      "第 14/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9726 Top-1 准确率: 0.7532 Top-5 准确率: 0.9688\n",
      "val 损失: 0.9354 Top-1 准确率: 0.7441 Top-5 准确率: 0.9335\n",
      "\n",
      "第 15/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9704 Top-1 准确率: 0.7537 Top-5 准确率: 0.9712\n",
      "val 损失: 0.9472 Top-1 准确率: 0.7428 Top-5 准确率: 0.9316\n",
      "\n",
      "第 16/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9471 Top-1 准确率: 0.7603 Top-5 准确率: 0.9720\n",
      "val 损失: 0.9394 Top-1 准确率: 0.7438 Top-5 准确率: 0.9326\n",
      "\n",
      "第 17/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9581 Top-1 准确率: 0.7570 Top-5 准确率: 0.9727\n",
      "val 损失: 0.9363 Top-1 准确率: 0.7471 Top-5 准确率: 0.9329\n",
      "\n",
      "第 18/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9535 Top-1 准确率: 0.7579 Top-5 准确率: 0.9729\n",
      "val 损失: 0.9272 Top-1 准确率: 0.7481 Top-5 准确率: 0.9342\n",
      "\n",
      "第 19/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9431 Top-1 准确率: 0.7601 Top-5 准确率: 0.9742\n",
      "val 损失: 0.9438 Top-1 准确率: 0.7446 Top-5 准确率: 0.9338\n",
      "\n",
      "第 20/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9330 Top-1 准确率: 0.7621 Top-5 准确率: 0.9747\n",
      "val 损失: 0.9383 Top-1 准确率: 0.7462 Top-5 准确率: 0.9326\n",
      "\n",
      "第 21/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9401 Top-1 准确率: 0.7599 Top-5 准确率: 0.9755\n",
      "val 损失: 0.9250 Top-1 准确率: 0.7491 Top-5 准确率: 0.9336\n",
      "\n",
      "第 22/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9375 Top-1 准确率: 0.7602 Top-5 准确率: 0.9762\n",
      "val 损失: 0.9218 Top-1 准确率: 0.7470 Top-5 准确率: 0.9359\n",
      "\n",
      "第 23/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9434 Top-1 准确率: 0.7577 Top-5 准确率: 0.9752\n",
      "val 损失: 0.9453 Top-1 准确率: 0.7457 Top-5 准确率: 0.9323\n",
      "\n",
      "第 24/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9244 Top-1 准确率: 0.7640 Top-5 准确率: 0.9770\n",
      "val 损失: 0.9479 Top-1 准确率: 0.7410 Top-5 准确率: 0.9328\n",
      "\n",
      "第 25/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9264 Top-1 准确率: 0.7626 Top-5 准确率: 0.9773\n",
      "val 损失: 0.9441 Top-1 准确率: 0.7467 Top-5 准确率: 0.9323\n",
      "\n",
      "第 26/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9146 Top-1 准确率: 0.7649 Top-5 准确率: 0.9795\n",
      "val 损失: 0.9462 Top-1 准确率: 0.7480 Top-5 准确率: 0.9308\n",
      "\n",
      "第 27/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9118 Top-1 准确率: 0.7662 Top-5 准确率: 0.9792\n",
      "val 损失: 0.9436 Top-1 准确率: 0.7448 Top-5 准确率: 0.9322\n",
      "\n",
      "第 28/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8893 Top-1 准确率: 0.7725 Top-5 准确率: 0.9789\n",
      "val 损失: 0.9533 Top-1 准确率: 0.7434 Top-5 准确率: 0.9314\n",
      "\n",
      "第 29/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8903 Top-1 准确率: 0.7720 Top-5 准确率: 0.9795\n",
      "val 损失: 0.9415 Top-1 准确率: 0.7487 Top-5 准确率: 0.9333\n",
      "\n",
      "训练完成，耗时 49m 41s\n",
      "最佳验证集准确率: 0.749100\n"
     ]
    }
   ],
   "source": [
    "model3,(train_losses3, train_top1_accs3, train_top5_accs3, val_losses3, val_top1_accs3, val_top5_accs3) = train_model(model2,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 30)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model3, '90epoch_highway+02drop.pth')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里的隐藏层是1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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